Harnessing AI and Machine Learning for Geospatial Analysis

About this course

Satellite data is everywhere. Most of it is still being looked at by eye.

Every week another dataset lands - imagery, sensor readings, crop surveys - and the analysis stops at a map you inspect manually. This course is about the other option: training models that classify, predict and count for you, across both Python and R.It is a broad course, deliberately. You will work in R and Python side by side, because real geospatial teams use both, and you will see where each one is the better tool. On the Python side: Pandas for spatial tables, remote sensing indices, zonal statistics, and three lectures on visualisation. On the R side: data structures, import and export, manipulation, packages and multiple linear regression.

Then the machine learning proper - a five-part hands-on project taking raw geospatial data through to a trained model, followed by a crop health classifier. Deep learning comes next: neural networks in R, then a convolutional neural network built in PyTorch for image classification.

The advanced work

Setting up GPU acceleration for training

Improving crop classification accuracy with Google Earth EngineAdvanced techniques for classifying complex geospatial data

Detecting and counting individual plants with computer visionA four-part air quality monitoring case study using real data from India

What you get

Over five hours of hands-on work across 44 lectures

Five quizzes covering R, Python, machine learning, deep learning and applications

Real case studies, not synthetic datasets

Bonus resources for continuing after the course

Before you enrol - please read

This is an intermediate course and it covers a lot of ground. You should already have written some code in Python or R; the language sections are a refresher and a bridge between th

Prices, discounts and availability are set by Udemy. We may earn a commission when you purchase through links on this site.